import numpy as np
import platgo as pg
import scipy.io as sio

def rastrigin(X: np.ndarray) -> np.ndarray:
    X = 0.0512 * X
    return np.sum(X ** 2 - 10 * np.cos(2 * np.pi * X) + 10, axis=1)

def Griewank(X: np.ndarray) -> np.ndarray:
    X = 6 * X
    return np.sum(X ** 2, axis=1) / 4000 - np.prod(np.cos(X) / np.tile(np.sqrt(np.arange(X.shape[1]) + 1), (X.shape[0], 1)), axis=1) + 1

def Schwefel(X: np.ndarray) -> np.ndarray:
    X = 10 * X + (4.2097e+2)
    g = X * np.sin(np.sqrt(np.abs(X)))
    temp = 500 - np.mod(X[X > 500], 500)
    g[X > 500] = temp * np.sin(np.sqrt(np.abs(temp))) - (X[X > 500] - 500) ** 2 / 10000 / X.shape[1]
    temp = np.mod(np.abs(X[X < -500]), 500) - 500
    g[X < -500] = temp * np.sin(np.sqrt(np.abs(temp))) - (X[X < -500] - 500) / 10000 / X.shape[1]
    return 418.9829 * X.shape[1] - np.sum(g, axis=1)

class CEC_2020_F8(pg.Problem):

    def __init__(self, D=None) -> None:
        self.name = 'CEC_2020_F8'
        self.type['single'], self.type['real'] = [True] * 2
        self.M = 1
        load_path = 'CEC2020.mat'
        load_data = sio.loadmat(load_path)
        mat = []
        for k in load_data.items():
            mat.append(k)
        self.D = D
        self.O = mat[3][1][0][7][0][0][0]
        if self.D is None or self.D < 10:
            self.D = 5
            self.Mat = mat[3][1][0][7][0][0][1]
        elif self.D < 15:
            self.D = 10
            self.Mat = mat[3][1][0][7][0][0][2]
        elif self.D < 20:
            self.D = 15
            self.Mat = mat[3][1][0][7][0][0][3]
        else:
            self.D = 20
            self.Mat = mat[3][1][0][7][0][0][4]
        lb = [-100] * self.D
        ub = [100] * self.D
        self.borders = np.array([lb, ub])
        super().__init__()

    def cal_obj(self, pop: pg.Population) -> None:
        Lambda = np.array([1, 10, 1])
        delta = np.array([10, 20, 30])
        bias = np.array([0, 100, 200])
        W = np.zeros((pop.decs.shape[0], 3))
        F = np.zeros(W.shape)
        for i in range(W.shape[1]):
            tmp = np.sum((pop.decs - np.tile(self.O[i, 0: pop.decs.shape[1]], (pop.decs.shape[0], 1))) ** 2, axis=1)
            W[:, i] = 1 / (np.sqrt(tmp) + (1e-10)) * np.exp(-tmp / 2 / self.D / delta[i] ** 2)
            if i == 0:
                F[:, i] = rastrigin(np.dot((pop.decs - np.tile(self.O[i, 0: pop.decs.shape[1]], (pop.decs.shape[0], 1))),
                                           self.Mat[0: self.D, :].T))
            if i == 1:
                F[:, i] = Griewank(np.dot((pop.decs - np.tile(self.O[i, 0: pop.decs.shape[1]], (pop.decs.shape[0], 1))),
                                           self.Mat[i * self.D: (i + 1) * self.D, :].T))

            if i == 2:
                F[:, i] = Schwefel(np.dot((pop.decs - np.tile(self.O[i, 0: pop.decs.shape[1]], (pop.decs.shape[0], 1))),
                                           self.Mat[i * self.D: (i + 1) * self.D, :].T))
        W = W / np.tile(np.sum(W, axis=1).reshape(np.sum(W, axis=1).shape[0], 1), (1, W.shape[1]))
        pop.objv = 2200 + np.sum(W * (np.tile(Lambda, (F.shape[0], 1)) * F + np.tile(bias, (F.shape[0], 1))), axis=1)
        pop.objv = pop.objv.reshape(pop.objv.shape[0], 1)

    def get_optimal(self) -> np.ndarray:
        pass


if __name__ == '__main__':
    problem = CEC_2020_F8()
    alg = pg.algorithms.GA(problem=problem, maxgen=100)
    pop = alg.go(100)
    print(pop)

